Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds that it probes each entity with ai_visibility_check and returns a ranked list with score, confidence, signal density. It does not contradict annotations and provides valuable behavioral context beyond the structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loaded with the core action, and every sentence adds value. No wasted words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has moderate complexity, 4 parameters, no output schema, and annotations cover safety, the description adequately explains the return format (ranked list with score, confidence, signal density) and the probing mechanism. It is sufficient for an agent to understand what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear descriptions for each parameter. The description adds important context: the first entity in the entities array is treated as the 'subject' for narrative and the rest as competitors. This adds semantic meaning beyond the schema for the crucial entities parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it compares AI visibility across multiple entities, probes each with ai_visibility_check, ranks them, and surfaces most/least recognized. The verb 'compare' and resource 'AI presence' make the purpose specific. It distinguishes from sibling ai_visibility_check (single entity) and compare_entities (generic comparison).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a use case (competitive AI-marketing audits) and an example question. It implicitly suggests using ai_visibility_check for single entities, but does not explicitly list alternatives or when not to use this tool. The context is clear enough for most agents.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.